"The error:When i carry out the FeatureSelection"

blueskybird2009

When I carry out the FeatureSelection, there come out a error.
Error:Operator has 0 children, should be between 1 and infinity
I don't know what's this error, how to solve?
hoping you to give me some advices.

My configuration file:
<operator name="Root" class="Process" expanded="yes">
    <operator name="TextInput" class="TextInput" expanded="yes">
        <list key="texts">
          <parameter key="graphics" value="D:\rm_workspace4.6\sample\data\20news\train\comp.graphics"/>
          <parameter key="hardware" value="D:\rm_workspace4.6\sample\data\20news\train\comp.sys.ibm.pc.hardware"/>
        </list>
        <parameter key="default_content_language" value="english"/>
        <parameter key="output_word_list" value="../data/training_words.md"/>
        <list key="namespaces">
        </list>
        <operator name="StringTokenizer" class="StringTokenizer">
        </operator>
        <operator name="EnglishStopwordFilter" class="EnglishStopwordFilter">
        </operator>
        <operator name="TokenLengthFilter" class="TokenLengthFilter">
        </operator>
        <operator name="PorterStemmer" class="PorterStemmer">
        </operator>
    </operator>
    <operator name="FeatureSelection" class="FeatureSelection" expanded="yes">
        <parameter key="show_stop_dialog" value="true"/>
        <parameter key="show_population_plotter" value="true"/>
    </operator>
    <operator name="LibSVMLearner" class="LibSVMLearner">
        <parameter key="kernel_type" value="linear"/>
        <list key="class_weights">
        </list>
    </operator>
    <operator name="ModelWriter" class="ModelWriter">
        <parameter key="model_file" value="../data/training_model.mod"/>
        <parameter key="output_type" value="Binary"/>
    </operator>
</operator>

land

Hi,
the feature selection is a so called meta operator, executing inner operators in order to estimate the performance of the current feature subset.
Here's an example how it works:

<operator name="Root" class="Process" expanded="yes">
    <description text="#ylt#p#ygt# Transformations of the attribute space may ease learning in a way, that simple learning schemes may be able to learn complex functions. This is the basic idea of the kernel trick. But even without kernel based learning schemes the transformation of feature space may be necessary to reach good learning results. #ylt#/p#ygt#  #ylt#p#ygt# RapidMiner offers several different feature selection, construction, and extraction methods. This selection process (the well known forward selection) uses an inner cross validation for performance estimation. This building block serves as fitness evaluation for all candidate feature sets. Since the performance of a certain learning scheme is taken into account we refer to processes of this type as #yquot#wrapper approaches#yquot#.#ylt#/p#ygt#  #ylt#p#ygt#Additionally the process log operator plots intermediate results. You can inspect them online in the Results tab. Please refer to the visualization sample processes or the RapidMiner tutorial for further details.#ylt#/p#ygt#  #ylt#p#ygt# Try the following: #ylt#ul#ygt# #ylt#li#ygt#Start the process and change to #yquot#Result#yquot# view. There can be a plot selected. Plot the #yquot#performance#yquot# against the #yquot#generation#yquot# of the feature selection operator.#ylt#/li#ygt# #ylt#li#ygt#Select the feature selection operator in the tree view. Change the search directory from forward (forward selection) to backward (backward elimination). Restart the process. All features will be selected.#ylt#/li#ygt# #ylt#li#ygt#Select the feature selection operator. Right click to open the context menu and repace the operator by another feature selection scheme (for example a genetic algorithm).#ylt#/li#ygt# #ylt#li#ygt#Have a look at the list of the process log operator. Every time it is applied it collects the specified data. Please refer to the RapidMiner Tutorial for further explanations. After changing the feature selection operator to the genetic algorithm approach, you have to specify the correct values. #ylt#table#ygt##ylt#tr#ygt##ylt#td#ygt##ylt#icon#ygt#groups/24/visualization#ylt#/icon#ygt##ylt#/td#ygt##ylt#td#ygt##ylt#i#ygt#Use the process log operator to log values online.#ylt#/i#ygt##ylt#/td#ygt##ylt#/tr#ygt##ylt#/table#ygt# #ylt#/li#ygt# #ylt#/ul#ygt# #ylt#/p#ygt#"/>
    <operator name="Input" class="ExampleSource">
        <parameter key="attributes"	value="../data/polynomial.aml"/>
    </operator>
    <operator name="FS" class="FeatureSelection" expanded="yes">
        <operator name="XValidation" class="XValidation" expanded="yes">
            <parameter key="sampling_type"	value="shuffled sampling"/>
            <operator name="NearestNeighbors" class="NearestNeighbors">
                <parameter key="k"	value="5"/>
            </operator>
            <operator name="ApplierChain" class="OperatorChain" expanded="yes">
                <operator name="Applier" class="ModelApplier">
                    <list key="application_parameters">
                    </list>
                </operator>
                <operator name="Performance" class="Performance">
                </operator>
            </operator>
        </operator>
        <operator name="ProcessLog" class="ProcessLog">
            <list key="log">
              <parameter key="generation"	value="operator.FS.value.generation"/>
              <parameter key="performance"	value="operator.FS.value.performance"/>
            </list>
        </operator>
    </operator>
</operator>

By the way: This examples is part of rapid miner and if you want to learn how things work, you should go through all these examples inside the sample directory of your workspace.

Greetings,
  Sebastian